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A new spike-in-based method for quantitative metabarcoding of soil fungi and bacteria

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Abstract

Metabarcoding is a powerful tool to characterize biodiversity in biological samples. The interpretation of taxonomic profiles from metabarcoding data has been hindered by their compositional nature. Several strategies have been proposed to transform compositional data into quantitative data, but they have intrinsic limitations. Here, I propose a workflow based on bacterial and fungal cellular internal standards (spike-ins) for absolute quantification of the microbiota in soil samples. These standards were added to the samples before DNA extraction in amounts estimated after qPCRs, to target around 1–2% coverage in the sequencing run. In bacteria, proportions of spike-in reads in the sequencing run were very similar (< 2-fold change) to those predicted by the qPCR assessment, and for fungi they differed up to 40-fold. The low variation among replicates highlights the reproducibility of the method. Estimates based on multiple bacterial spike-ins were highly correlated (r = 0.99). Procrustes analysis evidenced significant biological effects on the community composition when normalizing compositional data. A protocol based on qPCR estimation of input amounts of cellular spikes is proposed as a cheap and reliable strategy for quantitative metabarcoding of biological samples.

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Data availability

Raw FASTQ files and assembled sequences have been deposited in the NCBI under the BioProject PRJNA883611, with the accessions KHUY00000000 for fungal ITS2 data and KHUZ00000000 for bacterial 16S rRNA. Code and phyloseq objects with data were deposited in https://github.com/csmiguel/spike-in (Zenodo: 10.5281/zenodo.8302971).

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Acknowledgements

Funding came from the IFAPA grant PP.AVA.AVA2019.034, financed by the Junta de Andalucía with 80% FEDER funds from the European Union. The Spanish Collection of Type Cultures (CECT; http://www.uv.es), Valencia, supplied Yarrowia lipolytica strain CECT 1240. MC-S has a postdoctoral contract from the Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020). Part of the analysis was done in the High-Performance Computing cluster hosted by the Centro Informático Científico de Andalucía, CICA (https://www.cica.es/). Dr. Nieves Capote facilitated access to part of the laboratory equipment. María Camacho provided comments to the manuscript. Berta de los Santos and María Camacho and Luis Miranda led the field work.

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  1. Instituto Andaluz de Investigación y Formación Agraria, Pesquera, Alimentaria y de la Producción Ecológica (IFAPA) Centro Las Torres, Alcalá del Río, 41200, Seville, Spain

    Miguel Camacho-Sanchez

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Supplementary information

ESM 1

Supplementary Information S1: preparation of fungus spike-in with Yarrowia lipolytica. (DOCX 15 kb)

ESM 2

Supplementary Information S2: Excel template for estimating input spike-in to add to biological samples from qPCR results. (XLSX 15 kb)

ESM 3

Supplementary Information S3: ITSx workflow and its comparison with the main dada2 workflow. (DOCX 38 kb)

ESM 4

Supplementary Information S4: Estimation in the dataset of the input spike-in to add to biological samples from qPCR results. (XLSX 24 kb)

ESM 5

Supplementary Information S5: Sequencing output, filtered reads, wild/spike-in reads, and alpha diversity for each sample. (XLSX 6 kb)

ESM 6

Supplementary Information S6: NCBI SRA and BioSample accessions for each sample. (XLSX 9 kb)

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Camacho-Sanchez, M. A new spike-in-based method for quantitative metabarcoding of soil fungi and bacteria. Int Microbiol (2023). https://doi.org/10.1007/s10123-023-00422-5

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